Flexible Control Approach for DC Microgrid Oriented Electric Vehicle Charging Station

Abstract

Photovoltaic (PV) and wind-based intermittent dis-tributed energy resources have a negative impact on the quality of the power supply of the DC microgrid-oriented electric vehicle charging station, resulting in numerous control issues. The DC link voltage of the DC microgrid can be automatically balanced and monitored by properly coordinating the operation of each energy source and storage device. In this paper, the converter controller parameters of the individual subsystems of the DC microgrid (i.e., wind, PV system, battery, fuel cell, and electrolyzer) are designed using the state-space transfer function tool to solve system complexity and handle the intermittent nature of renewable energy. Firefly algorithm combined with particle swarm optimization (FA-PSO) is used to design the DC microgrid controller to reduce/mitigate DC voltage fluctuations. The ability of the proposed control strategy to withstand changes in solar in-solation, wind speed, and load perturbation is evaluated. For the DC microgrid controller design, TS-fuzzy, gray wolf optimization (GWO), and an adaptive neuro-fuzzy inference system assisted by particle swarm optimization (ANFIS-PSO) controllers are all compared and validated through hardware implementation. The results show that the proposed FA - PSO controller outperforms the other control strategies in terms of performance.